Nano-scale quantum dot simulations result in large-scale polynomial eigenvalue problems. It remains unclear how these problems can be solved efficiently. We fill this gap in capability partially by proposing a polynomial Jacobi-Davidson method framework, including several varied schemes for solving the associated correction equations. We investigate the performance of the proposed Jacobi-Davidson methods for solving the polynomial eigenvalue problems and several Krylov subspace methods for solving the linear eigenvalue problems with the use of various linear solvers and preconditioning schemes. This study finds the most efficient scheme combinations for different types of target problems.
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